Forecasting cross-border power exchanges through an HVDC line using dynamic modelling

• As smart grids develop, power systems become more complex, and the role of data gain considerable importance for the reliability of power supply. Thus, data processing techniques have to be investigated and compared to increase the efficiency of asset management decisions. In this paper, we explore several black-box models in order to predict power exchanges through a high-voltage direct-current line between Sweden and Denmark, using publicly available data on loads and power prices. An auto-regressive moving average with external input model based on load data provides the most accurate forecasts according to mean square error and other selected criteria. This is the first step to build a more comprehensive model that will also include other technical data such as maintenance and unplanned outages, but also macroeconomic factors. The final goal is to provide network operators with a parsimonious sequential model composed of several modules giving accurate predictions that support efficient asset management decisions. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

forecasting

loads

power exchanges

power prices

time series

transmission

Asset management

Big data

Costs

Data handling

Decision making

Electric power transmission networks

HVDC power transmission

Loading

Mean square error

Transmissions

Autoregressive moving average

Comprehensive model

Data processing techniques

High voltage direct current

Management decisions

Power exchange

Power price

Reliability of power supply

Smart power grids

Publikations- och innehållstyp

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